Object detection may be used to determine whether and/or where in an image a certain object such as a human face, a human body, an automobile, or the like appears. For example, face detection may be used in human-computer interaction, photo album management, biometrics, video surveillance, automatic focus in camera imaging, image or video search and retrieval, and the like.
Several object detection (e.g., face detection) solutions have been proposed including a Viola-Jones framework that may use Haar-like features and a decision tree weak classifier scheme and a SURF-like feature cascade technique that may use SURF-like features and a cascade classifier scheme. Although SURF-like techniques may have improved upon the Viola-Jones framework, implementations may not be suitable to real-time processing of higher resolution images such as high definition (HD), Full-HD (e.g., 1080p), or 4K resolution (e.g., content having horizontal resolution on the order of 4,000 pixels) using state of the art computing systems. Furthermore, such implementations may require large portions of memory (e.g., up to 32 times the input image size) during detection. Therefore, implementations may not be suitable to mobile devices or deep embedded devices or the like.
As such, existing techniques do not provide object detection for real-time processing on higher resolution images and/or solutions for mobile devices or deep embedded devices. Such problems may become critical as object detection becomes more widespread.